Image deformation is an operation often performed in computer graphics. One application where image deformation is particularly useful is where objects in different images have to be registered, for example, to register objects in an atlas with objects obtained through magnetic resonance (MR) imaging. More specifically, in the field of neurosurgery a three-dimensional brain atlas is registered onto a volumetric MR image. Following the registration of the atlas and the brain image, brain functional image analysis, image-guided neurosurgery, and model-enhanced neuroradiology are facilitated through the resulting combined image.
Existing methods for image registration can be broadly classified into two classes, namely intensity based image registration and feature based image registration. In methods using intensity based image registration a transformation between two input images is sought that maximizes some intensity similarity measure between the two images. Such methods are easily automated as the transformation is derived directly from the intensity values of the image data when applied to the similarity measure. A substantial challenge, however, is the translation of an image-similarity function to a desired anatomic correspondence. In addition, a large amount of evenly distributed features confronts these methods with an extremely large search space and criterion landscapes with many local minima, leading to computing cost increase and precision degrading.
Compared to intensity based image registration methods, feature based image registration methods do not work with the image data directly. Feature based image registration methods operate by inserting an intermediate step wherein a set of structure-related homologous features are extracted. It is the matching of these structure-related homologous features which is then used for registration. The features used for guiding the deformation vary from individual points, lines, contours, surface patches, entire parameterized surfaces, and particular objects. The reliance on structural information makes these methods more anatomically relevant. However, the challenge with these methods lies with obtaining a sufficiently large number of features automatically to drive the deformation.
A need therefore exists for a registration method which does not require particular structures to be extract, and which preserves topology of structures during deformation of such structures.